wehub-resource-sync
83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
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""""This example demonstrates:
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prompt_with_sources - powerful abstraction to integrate various knowledge sources into a prompt
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"""
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import os
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from llmware.prompts import Prompt
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from llmware.setup import Setup
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from llmware.models import PromptCatalog
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from llmware.library import Library
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from llmware.retrieval import Query
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from llmware.configs import LLMWareConfig
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def prompt_with_sources(model_name, library_name):
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print(f"Example - prompt_with_sources - attaching several different knowledge sources to a Prompt directly.")
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library = Library().create_new_library(library_name)
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sample_files_path = Setup().load_sample_files(over_write=False)
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ingestion_folder_path = os.path.join(sample_files_path, "Agreements")
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parsing_output = library.add_files(ingestion_folder_path)
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local_file = "Apollo EXECUTIVE EMPLOYMENT AGREEMENT.pdf"
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prompter = Prompt().load_model(model_name)
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# Use #1 - add_source_document - parses the document in memory, filters the text chunks by query, and then
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# creates a 'source' context to be passed to the model
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print(f"\n#1 - add a source document file directly into a prompt")
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sources2 = prompter.add_source_document(ingestion_folder_path, local_file, query="base salary")
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prompt = "What is the base salary amount?"
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prompt_instruction="default_with_context"
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response = prompter.prompt_with_source(prompt=prompt, prompt_name=prompt_instruction)[0]["llm_response"]
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print (f"- Context: {local_file}\n- Prompt: {prompt}\n- LLM Response:\n{response}")
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prompter.clear_source_materials()
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# Use #2 - add_source_wikipedia - gets a source document from Wikipedia on Barack Obama,
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# and creates source context
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print(f"\n#2 - add a wikipedia article by api call by topic into a prompt")
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prompt = "Was Barack Obama the Prime Minister of Canada?"
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wiki_topic = "Barack Obama"
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prompt_instruction = "yes_no"
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sources3 = prompter.add_source_wikipedia(wiki_topic, article_count=1)
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response = prompter.prompt_with_source(prompt=prompt, prompt_name=prompt_instruction)[0]["llm_response"]
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print (f"- Context: {wiki_topic}\n- Prompt: {prompt}\n- LLM Response:\n{response}")
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prompter.clear_source_materials()
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# Use #3 - add_source_query_results - produces the same results as the first case, but runs a query on the library
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# and then adds the query results to the prompt which are concatenated into a source context
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print(f"\n#3 - run a query on a library and then pass the query results into a prompt")
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query_results = Query(library).text_query("base salary")
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prompt = "What is the annual rate of the base salary?"
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sources4 = prompter.add_source_query_results(query_results)
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response = prompter.prompt_with_source(prompt=prompt, prompt_name=prompt_instruction)[0]["llm_response"]
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print(f"- Context: {local_file}\n- Prompt: {prompt}\n- LLM Response:\n{response}")
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prompter.clear_source_materials()
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return 0
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if __name__ == "__main__":
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LLMWareConfig().set_active_db("sqlite")
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# this model is a placeholder which will run on local laptop - swap out for higher accuracy, larger models
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model_name = "llmware/bling-1b-0.1"
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library_name = "lib_prompt_with_sources_1"
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prompt_with_sources(model_name,library_name)
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